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Fault detection and classification in electrical power transmission system using artificial neural network.

Jamil M, Sharma SK, Singh R - Springerplus (2015)

Bottom Line: The different faults are simulated with different parameters to check the versatility of the method.The proposed method can be extended to the Distribution network of the Power System.The various simulations and analysis of signals is done in the MATLAB(®) environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India.

ABSTRACT
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

No MeSH data available.


Related in: MedlinePlus

Data pre processing illustration.
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Fig4: Data pre processing illustration.

Mentions: Figure 4 shows the current waveform of a Phase B—ground fault at a distance of 60 km from terminal A on a 300 km transmission line. The waveform is the plot of the samples obtained at a frequency of 1,000 Hz. The inputs used to the neural network are the ratios of the voltages and currents in each of the phases before and after the occurrence of fault. The advantage of performing this scaling is to reduce the training computation time.Figure 4


Fault detection and classification in electrical power transmission system using artificial neural network.

Jamil M, Sharma SK, Singh R - Springerplus (2015)

Data pre processing illustration.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496419&req=5

Fig4: Data pre processing illustration.
Mentions: Figure 4 shows the current waveform of a Phase B—ground fault at a distance of 60 km from terminal A on a 300 km transmission line. The waveform is the plot of the samples obtained at a frequency of 1,000 Hz. The inputs used to the neural network are the ratios of the voltages and currents in each of the phases before and after the occurrence of fault. The advantage of performing this scaling is to reduce the training computation time.Figure 4

Bottom Line: The different faults are simulated with different parameters to check the versatility of the method.The proposed method can be extended to the Distribution network of the Power System.The various simulations and analysis of signals is done in the MATLAB(®) environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, Faculty of Engineering, Jamia Millia Islamia, New Delhi, 110025 India.

ABSTRACT
This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

No MeSH data available.


Related in: MedlinePlus